Story
The visualisations provided below offer a range of insights into various aspects of countries worldwide. From economic indicators such as GDP and inflation rates to health-related issues such as Vitamin A deficiency, these visualisations help to paint a picture of different countries’ conditions. Moreover, by presenting data in an easily understandable format, these visualisations can help policymakers and individuals make informed decisions and gain a deeper understanding of the challenges facing different nations. The dataset helps to compare the GDP of the countries in the world over the period of time. The Time Series visualisation allows the viewer to easily see the relative size of the GDPs of the countries and the observation values of Vitamin Deficiency. The bar graph displays the trend in Vitamin A deficiency. The chart shows the stacked version by GDP and observation Values for Vitamin deficiency. This visualisation helps the viewer understand the prevalence of Vitamin A deficiency across different time frame and how it has changed over time along with the GDP values in 10 thousand for respective year. In conclusion, the visualisations presented below provide valuable insights into the conditions of different countries worldwide. They highlight the wide range of challenges these nations face, from economic issues such as inflation and GDP growth and health issues such as Vitamin A deficiency. By providing a clear and concise view of the data, these visualisations help to facilitate better decision-making and a deeper understanding of the issues facing different nations. These visualisations are essential for policymakers and individuals, providing valuable insights into the world’s diverse and complex conditions.
#Importing Files
indict_1_grpd <- read.csv("/cloud/project/unichef_data_indicator_1_grpd.csv", header = TRUE)
View(indict_1_grpd)
indict_1_grpd$population_million <- indict_1_grpd$Population/ 1000000
indict_1_grpd$gdp_in_10k <- indict_1_grpd$GDP/ 10000
indict_1_grpd$obs_val_in_10k <- indict_1_grpd$obs_true_val / 10000#Time Series Code
time_s <- select(indict_1_grpd, time_period, population_million, obs_true_val, GDP)
View(time_s)
time_s_grp <- time_s %>% group_by(time_period) %>%
summarise(avg_population_million= mean(population_million),
avg_obs_true_val = mean(obs_true_val),
avg_gdp = mean(GDP),
.groups = 'drop')
View(time_s_grp)
f_obs_val<- ggplot(data = time_s_grp, aes(x = time_period, y = avg_obs_true_val))+ geom_line(color = "black", size = 2) +
theme(panel.background = element_rect(fill='grey'))+
scale_x_continuous(breaks=seq(2000,2020, by = 2))+
ggtitle("Time Series by observation value of Vitamin Deficency") +
xlab("year") + ylab("obs_val")
f_gdp<- ggplot(data = time_s_grp, aes(x = time_period, y = avg_gdp))+ geom_line(color = "black", size = 2)+
theme(panel.background = element_rect(fill='grey'))+
scale_x_continuous(breaks=seq(2000,2020, by = 2))+
ggtitle("Time Series by GDP of Vitamin Deficency") +
xlab("year") + ylab("gdp")
grid.arrange(f_obs_val,f_gdp, ncol = 1)
#Bar Plot
indict_1_grp_new <- indict_1_grpd %>% group_by(time_period) %>%
summarise(mean_obs_true_val = mean(obs_true_val),
mean_gdp = mean(GDP),
.groups = 'drop')
View(indict_1_grp_new)
fig_1 <- plot_ly(indict_1_grp_new, x = ~time_period, y = ~mean_obs_true_val, type = 'bar', name = 'obs_val')
fig_1 <- fig_1 %>% add_trace(y = ~mean_gdp, name = 'gdp in 10k')
fig_1 <- fig_1 %>% layout(title = 'Year by Obs Value & Gdp of Vitamin Deficiency', plot_bgcolor = "#e5ecf6",
xaxis = list(title = 'Year'),yaxis = list(title = 'Values'), barmode = 'stack')%>%
layout(xaxis = list(categoryorder = "total descending"))
fig_1#Scatter Plot
indict_1_grp_sctr <- indict_1_grpd %>% group_by(country) %>%
summarise(mean_obs_true_val = mean(obs_true_val),
mean_gdp = mean(GDP),
mean_population_million = mean(population_million),
.groups = 'drop')
View(indict_1_grp_sctr)
fit_model <- lm(mean_obs_true_val ~ mean_gdp, data = indict_1_grp_sctr)
indict_1_grp_sctr %>%
plot_ly(x = ~mean_gdp) %>%
add_markers(y = ~mean_obs_true_val) %>%
add_lines(x = ~mean_gdp, y = fitted(fit_model))%>%
layout(showlegend = F)%>%
layout(title = 'Scatter plot of Mean GDP and Mean Observation Values', plot_bgcolor = "#e5ecf6")#World Map by obs value and countries
wd <- map_data('world') %>% fortify
View(wd)
wrld_data_df <- select(indict_1_grpd, region = country, "obs_value" = obs_true_val)
View(wrld_data_df)
world_df_1 <- left_join(wrld_data_df, wd, by = "region")## Warning in left_join(wrld_data_df, wd, by = "region"): Detected an unexpected many-to-many relationship between `x` and `y`.
## ℹ Row 1 of `x` matches multiple rows in `y`.
## ℹ Row 11 of `y` matches multiple rows in `x`.
## ℹ If a many-to-many relationship is expected, set `relationship =
## "many-to-many"` to silence this warning.View(world_df_1)
fig_map <- ggplot() +
geom_map(data = wd, map = wd,
aes(x = long, y = lat, group = group, map_id=region),
fill = "white", colour = "#7f7f7f", size=0.5) +
geom_map(data = wrld_data_df, map=wd,
aes(fill=obs_value,map_id=region),
colour="#7f7f7f", size=0.5) + geom_text_repel() +
coord_map("rectangular", lat0=0, xlim=c(-180,180), ylim=c(-60, 90)) +
scale_fill_continuous(low="thistle2", high="darkred", guide="colorbar") +
scale_y_continuous(breaks=c()) +
scale_x_continuous(breaks=c()) +
labs(fill="Obs Value", title="World Map", x="", y="") +
theme_bw() ## Warning in geom_map(data = wd, map = wd, aes(x = long, y = lat, group = group,
## : Ignoring unknown aesthetics: x and yfig_map